ALinrunrun opened a new issue, #19533:
URL: https://github.com/apache/tvm/issues/19533
### Expected behavior
TVM Relax should execute ONNX `Clip` consistently with ONNX Runtime when the
`max` bound is `NaN`.
For the following inputs:
```
X = [0.5, -3.0, 4.5, 11.0, NaN]
MN = 0.0
MX = NaN
```
ONNX Runtime returns:
`[0.5, 0.0, 4.5, 11.0, nan]`
### Actual behavior
TVM Relax returns NaN for every element:
```
ORT: [0.5, 0.0, 4.5, 11.0, nan]
TVM: [nan, nan, nan, nan, nan]
```
The discrepancy appears when importing an ONNX Clip model through the Relax
ONNX frontend and compiling it for the llvm target.
### Environment
```
TVM: 0.14 environment / Relax ONNX frontend
ONNX Runtime: 1.23
Python: 3.11
Target: llvm
OS: Linux
```
### Steps to reproduce
```
import numpy as np
import onnx
import onnxruntime as ort
from onnx import TensorProto, helper
import tvm
from tvm import relax
from tvm.relax.frontend.onnx import from_onnx
node = helper.make_node("Clip", ["X", "MN", "MX"], ["Y"])
graph = helper.make_graph(
[node],
"g",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, [5]),
helper.make_tensor_value_info("MN", TensorProto.FLOAT, []),
helper.make_tensor_value_info("MX", TensorProto.FLOAT, []),
],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [5])],
)
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 17)])
model.ir_version = 9
x = np.array([0.5, -3.0, 4.5, 11.0, np.nan], dtype=np.float32)
mn = np.array(0.0, dtype=np.float32)
mx = np.array(np.nan, dtype=np.float32)
ort_out = ort.InferenceSession(
model.SerializeToString(),
providers=["CPUExecutionProvider"],
).run(None, {"X": x, "MN": mn, "MX": mx})[0]
mod = from_onnx(model, keep_params_in_input=False)
with tvm.transform.PassContext(opt_level=3):
ex = tvm.compile(mod, target=tvm.target.Target("llvm"))
vm = relax.VirtualMachine(ex, tvm.cpu())
out = vm["main"](
tvm.runtime.tensor(x, tvm.cpu()),
tvm.runtime.tensor(mn, tvm.cpu()),
tvm.runtime.tensor(mx, tvm.cpu()),
)
tvm_out = (out[0] if isinstance(out, (list, tuple)) else out).numpy()
print("ORT:", ort_out.tolist())
print("TVM:", tvm_out.tolist())
```
### Triage
* needs-triage
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